import gc
import os
from typing import TypeAlias

import torch
from PIL.Image import Image
from diffusers import FluxPipeline, FluxTransformer2DModel, AutoencoderKL, AutoencoderTiny, DiffusionPipeline
from huggingface_hub.constants import HF_HUB_CACHE
from pipelines.models import TextToImageRequest
from torch import Generator
from torchao.quantization import quantize_, int8_weight_only
from transformers import T5EncoderModel, CLIPTextModel


Pipeline: TypeAlias = FluxPipeline
torch.backends.cudnn.benchmark = True
torch._inductor.config.conv_1x1_as_mm = True
torch._inductor.config.coordinate_descent_tuning = True
torch._inductor.config.epilogue_fusion = False
torch._inductor.config.coordinate_descent_check_all_directions = True
os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"

id = "black-forest-labs/FLUX.1-schnell"
revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"

vae_id = "madebyollin/taef1"
vae_revision = "2d552378e58c9c94201075708d7de4e1163b2689"


def load_pipeline() -> Pipeline:
    path = os.path.join(HF_HUB_CACHE, "models--freaky231--flux.1-schnell-int8/snapshots/c33fa7f79751fe42b0a7de7f72edb5d1b86f32a7/transformer")
    transformer = FluxTransformer2DModel.from_pretrained(
            path, 
            use_safetensors=False,
            local_files_only=True,
            torch_dtype=torch.bfloat16)
    vae = AutoencoderTiny.from_pretrained(
            vae_id,
            revision=vae_revision,
            local_files_only=True,
            torch_dtype=torch.bfloat16
            )    
    pipeline = DiffusionPipeline.from_pretrained(
        id,
        revision=revision,
        transformer=transformer,
        vae=vae,
        local_files_only=True,
        torch_dtype=torch.bfloat16,
    )

    pipeline.to(memory_format=torch.channels_last)
    pipeline.to("cuda")
    for _ in range(2):
        pipeline("satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper", num_inference_steps=4)

    return pipeline

@torch.inference_mode()
def infer(request: TextToImageRequest, pipeline: Pipeline, generator: torch.Generator) -> Image:
    generator = Generator(pipeline.device).manual_seed(request.seed)

    try:
        prompt = request.prompt
    except Exception as e:
        prompt = "satiety, unwitherable, Pygmy, ramlike, Curtis, fingerstone, rewhisper"
        
    return pipeline(
        prompt,
        generator=generator,
        guidance_scale=0.0,
        num_inference_steps=4,
        max_sequence_length=256,
        height=request.height,
        width=request.width,
    ).images[0]


if __name__ == "__main__":
    pipe_ = load_pipeline()
    for _ in range(2):
        request = TextToImageRequest(prompt='dog', 
                height=None,
                width=None,
                seed=666)
        infer(request, pipe_)